Systems for identifying persons through intrinsic human traits have been developed. These systems operate by taking images of a physiological trait of a person and comparing information stored in the image to data that corresponds the trait for a particular person. When the information stored in the image has a high degree of correlation to the relevant data previously obtained a particular person's trait, a person can be positively identified. These biometric systems obtain and compare data for physical features, such as fingerprints, voice, and facial characteristics. Different traits impose different constraints on these systems. For example, fingerprint recognition systems require the person to be identified to contact an object directly for the purpose of obtaining fingerprint data from the object. Facial feature recognition systems, however, do not require direct contact with a person and these biometric systems are capable of capturing identification data without the cooperation of the person to be identified.
One trait especially suited for non-cooperative identification is an iris pattern in a person's eye. The human eye iris provides a unique trait that changes little over a person's lifetime. For cooperative iris recognition, the person to be identified is aware of an image being taken and the captured image is a frontal view of the eye. Non-cooperative iris image capture systems, on the other hand, obtain an iris image without a person's knowledge of the data capture. Thus, the subject's head is likely moving and his or her eyes are probably blinking during iris image acquisition. Consequently, the captured image is not necessarily a fully open frontal view of the eye.
Identification of a person from an iris image requires iris image segmentation. Segmentation refers to the relative isolation of the iris in the eye image from the other features of an eye or that are near an eye. For example, eyelashes and eyelids are a portion of an eye image, but they do not contribute to iris information that may be used to identify a person. Likewise, the pupil does not provide information that may be used to identify a person. Consequently, effective segmentation to locate the portions of a captured eye image that contain iris pattern information is necessary for reliable identification of person. Because previously known iris identification systems rely upon the acquisition of eye images from cooperative subjects, iris segmentation techniques have focused on frontal eye images.
Efforts have been made to develop iris image processing methods that accurately identify persons from iris images obtained by a non-cooperative image acquisition system. Once such method proposes use of a Fourier-based trigonometry for estimating two spherical components for an angle of gaze with an affine transformation being used to “correct” the image and center the gaze. This method has limited effectiveness because affine transformations assume the iris is planar, when in fact it has some curvature. The eye is a three dimensional object and the deformed images of iris patterns may present different correlations for the iris patterns. Use of a two dimensional feature extraction model to obtain images for recognition of a three dimensional object is not optimal. Some eyes have patterns that do not change very much when one's gaze changes and these eyes respond well to affine transformation analysis. In general, however, empirical data reveals that many iris patterns do change substantially with a change in gaze and, therefore, identification using images of these iris patterns require a different approach to iris recognition in a noncooperative environment.
Other issues also arise in the non-cooperative imaging of eyes. For example, iris images can be blurred, severely occluded, poorly illuminated and/or severely dilated in addition to presenting an off-angle view of the eye. As an iris gaze changes with respect to a camera lens, the size, shape, and relative centroids of the limbic and pupil regions may change. Given these variables that may lower the quality of iris images, one hundred percent accuracy in segmentation is extremely difficult, and segmentation error in the processing of the image may not be avoidable. Thus, an iris recognition method should be tolerant of segmentation error.
What is needed is a more robust method of identifying an iris from an off-angle view of an eye to identify correctly those portions of an eye image that contain iris pattern data in an eye image obtained from a non-cooperative eye image acquisition system.